• The Korean Journal of Applied Statistics
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• v.18 no.1
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• pp.173-182
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• 2005

A Bayesian threshold model is considered to analyze binary or ordered categorical traits. Gibbs sampler for making full Bayesian inferences about the category probability as well as the regression coefficients is described. The model can be regarded as an alternative to the ordered logit regression model. Numerical examples are shown to demonstrate the efficiency of the model.

• Preventive Nutrition and Food Science
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• v.16 no.2
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• pp.117-126
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• 2011

This study investigated potential causes of obesity by examining diet and lifestyle factors. The data from the 2008 Korea National Health and Nutrition Survey were statistically analyzed to determine the relative importance of causes of obesity. Because the factors affecting obesity for males and females were significantly different, binary choice logistic models of the male and female subjects were built and estimated separately. Our results show that stress, the irregularity of eating breakfast, and frequency of eating out had the three greatest impacts on male obesity, respectively, and stress, employment status, and age had the greatest impacts on female obesity, in that order.

• The Korean Journal of Community Living Science
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• v.27 no.1
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• pp.67-81
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• 2016

Responsible tourism has become an established area of the tourism industry. This study aims to identify the factors that influence re-participation in responsible tourism in rural Korea. On-site survey was conducted on 436 tourists by seven responsible tourism agencies in Korea. The motivation for responsible tourists was categorized into seven types: family togetherness, escape and relaxation, personal growth, social interaction, various experiences, learning, and natural experience. The estimation of a binary logistic regression model determined the characteristics of responsible tourists who are most likely to opt for re-participation in responsible tourism. Results indicated that important factors for re-participation in responsible tourism were 'age', 'educational level', 'accompany', 'length of stay', and 'motivation'. The results implied that tourists' internal and external factors are important for re-participation in responsible tourism. It is expected that this study will contribute to the market expansion of responsible tourism.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1191-1208
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• 2015

Most statistical methods are dependent on the summary statistic. However, with graphical approaches, it is easier to identify the characteristics of the data and detect information that cannot be obtained by the summary statistic. We present various graphical methods to assess the adequacy of models in logistic regression that include checking log-density ratio, structural dimension, marginal model plot, chi-residual plot, and CERES plot. Through simulation data, we investigate and compare the results of graphical approaches under diverse conditions.

• Journal of Digital Contents Society
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• v.19 no.4
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• pp.685-692
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• 2018

The purpose of this study is to develop a prediction model of hypertension in middle-aged adults using Statistical analysis. Statistical analysis and prediction models were developed using the National Health and Nutrition Survey (2013-2016).Binary logistic regression analysis showed statistically significant risk factors for hypertension, and a predictive model was developed using logistic regression and the Naive Bayes algorithm using Wrapper approach technique. In the statistical analysis, WHtR(p<0.0001, OR = 2.0242) in men and AGE (p<0.0001, OR = 3.9185) in women were the most related factors to hypertension. In the performance evaluation of the prediction model, the logistic regression model showed the best predictive power in men (AUC = 0.782) and women (AUC = 0.858). Our findings provide important information for developing large-scale screening tools for hypertension and can be used as the basis for hypertension research.

• Asia Pacific Journal of Business Review
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• v.3 no.2
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• pp.67-87
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• 2019

Anti-dumping instruments among trading partners have been the subject of research by both academicians and practitioners. This study attempts to establish an early-warning model of anti-dumping against Chinese textile exporting companies, which have suffered from anti-dumping regulations and got arbitration awards. After reviewing theories of anti-dumping arbitration, early-warning and relationship marketing, the measuring items and relationship marketing model of Chinese textiles exporters are investigated. Empirical methods are selected based on early-warning theories of companies. Eighty percent of 156 valid questionnaires by surveys and interviews are used as training data via Binary-Logistic regression while the other twenty percent are validated in the model. As a result, a proper early-warning model has been established.

• Journal of Korean Society of Transportation
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• v.31 no.6
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• pp.53-66
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• 2013

The major purpose of this study is to evaluate methodologies to predict the injury severity of pedestrian-vehicle collisions. Methodologies to be evaluated and compared in this study include Binary Logistic Regression(BLR), Ordered Probit Model(OPM), Support Vector Machine(SVM) and Decision Tree(DT) method. Valuable insights into applying methodologies to analyze the characteristics of pedestrian injury severity are derived. For the purpose of identifying causal factors affecting the injury severity, statistical approaches such as BLR and OPM are recommended. On the other hand, to achieve better prediction performance, heuristic approaches such as SVM and DT are recommended. It is expected that the outcome of this study would be useful in developing various countermeasures for enhancing pedestrian safety.

• Journal of Korean Society of Transportation
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• v.26 no.1
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• pp.155-164
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• 2008

Variable Message Signs (VMS) is one of the major components for Intelligent Transport Systems (ITS) services that provides real-time traffic and incident information to drivers. The objective of this research was to develop a method determining the optimal location of VMS considering safety and driving characteristics of various drivers. A driving simulator was utilized to evaluate how drivers can safely exit to off-ramp depending on various VMS locations while information relating route diversion was provided. The binary logistic regression and factor analysis were applied in developing a probability model that predicts the success of safe off-ramp exiting. Based on the developed probability model, a method to estimate the spacing between VMS and off-ramp is suggested. It is expected that the products of this study would be utilized as a tool in determining VMS locations for ITS planners and designers.

• Journal of Korean Society of Transportation
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• v.29 no.3
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• pp.73-82
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• 2011

Recent advancement in traffic surveillance systems has allowed the researchers to obtain more detailed vehicular movement such as individual vehicle trajectory data. Understanding the characteristics of interactions between leading and following vehicles in the traffic flow stream is a backbone for designing and evaluating more sophisticated traffic and vehicle control strategies. This study proposes a methodology for estimating rear-end crash potential, as a probabilistic measure, in real-time based on the analysis of vehicular movements. The methodology presented in this study consists of three components. The first predicts vehicle position and speed every second using a Kalman filtering technique. The second estimates the probability for the vehicle's trajectory to belong to either 'changing lane' or 'going straight'. A binary logistic regression (BLR) is used to model the lane-changing decision of the subject vehicle. The other component calculates crash probability by employing an exponential decay function that uses time-to-collision (TTC) between the subject vehicle and the front vehicle. The result of this study is expected to be adapted in developing traffic control and information systems, in particular, for crash prevention.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.1
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• pp.1-16
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• 2022

Recently, e-scooters have been attracting attention as eco-friendly modes of transportation in cities due to an increasing interest in the environment. Accordingly, various studies on usage behavior are being conducted, but studies that reflect individual travel attitudes are insufficient. Therefore, this study surveyed commuters in Seoul and analyzed respondents' traveling attitudes through factor analysis. It also built a binary logistic regression model for the intention to use shared e-scooters to determine how individual travel behaviors are affected. In particular, the model results showed that age, the main mode of transportation (car), walking time to the bus stop, and four travel attitude variables (disutility of travel, preference to self-drive, internet/smartphone friendliness, and willingness to pay extra money for services) significantly affected the intention to use shared e-scooters. This study is expected to be used as basic data, with aspect to travel behavior, for the efficient operation and use of shared e-scooters in the future.